The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models
Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, Nizar Habash
Abstract
In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.- Anthology ID:
- 2021.wanlp-1.10
- Original:
- 2021.wanlp-1.10v1
- Version 2:
- 2021.wanlp-1.10v2
- Volume:
- Proceedings of the Sixth Arabic Natural Language Processing Workshop
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine (Virtual)
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 92–104
- Language:
- URL:
- https://aclanthology.org/2021.wanlp-1.10
- DOI:
- Cite (ACL):
- Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, Houda Bouamor, and Nizar Habash. 2021. The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 92–104, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
- Cite (Informal):
- The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models (Inoue et al., WANLP 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.wanlp-1.10.pdf
- Code
- CAMeL-Lab/CAMeLBERT
- Data
- ASTD, OSCAR